Abstract
Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.
Original language | English |
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Article number | 224002 |
Number of pages | 8 |
Journal | Journal of Physics D: Applied Physics |
Volume | 51 |
Issue number | 22 |
DOIs | |
Publication status | Published - 6 Jun 2018 |
Funding
STK was supported by an Office of Technology Licensing Stanford Graduate Fellowship. AM gratefully acknowledges support from the Knut and Alice Wallenberg Foundation (KAW 2016.0494) for Postdoctoral Research at Stanford University. EJF and AAT were supported by Nanostructures for Electrical Energy Storage (NEES), an Energy Frontier Research Center (EFRC) funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DESC0001160 and by Sandia’s Laboratory-Directed Research and Development program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the article do not necessarily represent the views of the US Department of Energy or the United States Government. AS acknowledges financial support from the National Science Foundation, E2CDA Award #1739795.
Keywords
- electrochemical organic neuromorphic device
- neural network
- neuromorphic computing
- organic electronics
- PEDOT:PSS
- resistive memory
- symmetric cycling
- PEDOT: PSS